/**
* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/

/*!
 * \file fma.h
 * \brief
 */

#ifndef LIB_MATH_FMA_H
#define LIB_MATH_FMA_H

#if defined(__DAV_C310__) || defined(__DAV_310R6__) || (__NPU_ARCH__ == 5102)
#include "kernel_tensor.h"
#include "../../../impl/adv_api/detail/math/fma/fma_common_impl.h"

namespace AscendC {
#pragma begin_pipe(V)
/*!
 * \ingroup Fma
 * \brief compute Fma elementwisely
 * \tparam T: half/float
 * \param [out] dst: output LocalTensor
 * \param [in] src0: input LocalTensor
 * \param [in] src1: input LocalTensor
 * \param [in] src2: input LocalTensor
 * \param [in] sharedTmpBuffer: extra temporary shared space used for intermediate values among calculation process,
 *             whose required space size should refer to corresponding tiling API, which is defined at fma_tiling.h.
 *             Generally, the more space you allocate, the better performance you will achieve, and the performance
 *             reaches peak when buffer size is maximum(calculated by tiling function). Moreover, it is not guaranteed
 *             that the shared space will be cleared after usage, the data could be anything.
 * \param [in] count: the number of elements to be processed.
 * \note dst/src0/src1/src2 Tensor must be 32B aligned, and it doesn't allow dst/src0/src1/src2/sharedTmpBuffer tensor address overlap.
 */
template <const FmaConfig& config = DEFAULT_FMA_CONFIG, typename T>
__aicore__ inline void Fma(const LocalTensor<T>& dst, const LocalTensor<T>& src0,
    const LocalTensor<T>& src1, const LocalTensor<T>& src2, const LocalTensor<uint8_t>& sharedTmpBuffer,
    const uint32_t count)
{
    FmaImpl<config, T>(dst, src0, src1, src2, sharedTmpBuffer, count);
}

/*!
 * \ingroup Fma
 * \brief compute Fma elementwisely
 * \tparam T: half/float
 * \param [out] dst: output LocalTensor
 * \param [in] src0: input LocalTensor
 * \param [in] src1: input LocalTensor
 * \param [in] src2: input LocalTensor
 * \param [in] count: the number of elements to be processed.
 * \note dst/src0/src1/src2 Tensor must be 32B aligned, and it doesn't allow dst/src0/src1/src2/sharedTmpBuffer tensor address overlap.
 */
template <const FmaConfig& config = DEFAULT_FMA_CONFIG, typename T>
__aicore__ inline void Fma(const LocalTensor<T>& dst, const LocalTensor<T>& src0,
    const LocalTensor<T>& src1, const LocalTensor<T>& src2, const uint32_t count)
{
    FmaImpl<config, T>(dst, src0, src1, src2, count);
}

#pragma end_pipe
} // namespace AscendC
#endif
#endif // LIB_MATH_FMA_H
